DBMS > RDF4J vs. searchxml vs. Sphinx vs. Vertica

System Properties Comparison RDF4J vs. searchxml vs. Sphinx vs. Vertica

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Editorial information provided by DB-Engines

Name

Description

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

DBMS for structured and unstructured content wrapped with an application server

Open source search engine for searching in data from different sources, e.g. relational databases

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

RDF store

Native XML DBMS
Search engine

Search engine

Relational DBMS

Column oriented

Secondary database models

Spatial DBMS
Time Series DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.72
Rank	#222	Overall
	#9	RDF stores

Score	0.00
Rank	#385	Overall
	#7	Native XML DBMS
	#24	Search engines

Score	5.97
Rank	#56	Overall
	#5	Search engines

Score	9.62
Rank	#42	Overall
	#26	Relational DBMS

Website

rdf4j.org

www.searchxml.net/category/products

sphinxsearch.com

www.vertica.com

Technical documentation

rdf4j.org/documentation

www.searchxml.net/support/handouts

sphinxsearch.com/docs

vertica.com/documentation

Developer

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

informationpartners gmbh

Sphinx Technologies Inc.

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2004

2015

2001

2005

Current release

1.0

3.5.1, February 2023

12.0.3, January 2023

License

Commercial or Open Source

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

Open Source

GPL version 2, commercial licence available

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++

Server operating systems

Linux
OS X
Unix
Windows

Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux

Data scheme

yes

RDF Schemas

schema-free

yes

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

Typing

predefined data types such as float or date

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

full-text index on all search fields

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

SQL-like query language (SphinxQL)

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

RESTful HTTP API
WebDAV
XQuery
XSLT

Proprietary protocol

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

Java
PHP
Python

C++

most other programming languages supported via APIs

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

yes

on the application server

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Partitioning is done manually, search queries against distributed index is supported

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

sychronisation to multiple collections

none

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Isolation support depends on the API used

multiple readers, single writer

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

yes

The original contents of fields are not stored in the Sphinx index.

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

Domain, group and role-based access control at the document level and for application services

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

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More resources

DB-Engines blog posts

The DB-Engines ranking includes now search engines
4 February 2013, Paul Andlinger

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